Classification of Malicious Websites Using Machine Learning Based on URL Characteristics-Reference-Cited by-同舟云学术

Classification of Malicious Websites Using Machine Learning Based on URL Characteristics

Published:2023 Issue: Volume: Page:317-327
ISSN:0302-9743
Container-title:Computational Science and Its Applications – ICCSA 2023 Workshops
language:
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Author:

Ha Muon^ORCID,Shichkina Yulia^ORCID,Nguyen Nhan,Phan Thanh-Son

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-37129-5_26

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3. Hong, J., Kim, T., Liu, J., Park, N., Kim, S.-W.: Phishing URL detection with lexical features and blacklisted domains. In: Jajodia, S., Cybenko, G., Subrahmanian, V.S., Swarup, V., Wang, C., Wellman, M. (eds.) Adaptive Autonomous Secure Cyber Systems, pp. 253–267. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33432-1_12

4. Sun, B., Akiyama, M., Yagi, T., Hatada, M., Mori, T.: Automating URL blacklist generation with similarity search approach. IEICE Tran. Inf. Syst. E99(D), 873–882 (2016). https://doi.org/10.1587/transinf.2015ICP0027

5. Whittaker, C., Ryner, B., Nazif, M.: Large-scale automatic classification of phishing pages. In: Network and Distributed System Security Symposium (2010)

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